





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 						SCREAMING_SNAKE_CASE__							(			_UpperCAmelCase    ):
      A_		:							str				=       ['image_processor', 'tokenizer']
      A_		:							Optional[int]				=       'ViTImageProcessor'
      A_		:							int				=       ('CLIPTokenizer', 'CLIPTokenizerFast')


      def __init__(self			: Union[str, Any]							,     a__			: Optional[int]=None							,     a__			: Dict=None							,     **a__			: List[str] ):





         """simple docstring"""



         __snake_case							=  None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''							,     lowercase_							,     )
            __snake_case							=  kwargs.pop('''feature_extractor''' )

         __snake_case							=  image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.''' )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.''' )

         super().__init__(lowercase_							,     lowercase_ )


      def __call__(self			: Any							,     a__			: List[str]=None							,     a__			: int=None							,     a__			: Optional[Any]=None							,     a__			: Dict=None							,     **a__			: Dict ):





         """simple docstring"""



         if text is None and visual_prompt is None and images is None:
            raise ValueError('''You have to specify either text, visual prompt or images.''' )

         if text is not None and visual_prompt is not None:
            raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''' )

         if text is not None:
            __snake_case							=  self.tokenizer(lowercase_							,     return_tensors=lowercase_							,     **lowercase_ )

         if visual_prompt is not None:
            __snake_case							=  self.image_processor(lowercase_							,     return_tensors=lowercase_							,     **lowercase_ )

         if images is not None:
            __snake_case							=  self.image_processor(lowercase_							,     return_tensors=lowercase_							,     **lowercase_ )

         if visual_prompt is not None and images is not None:
            __snake_case							=  {
                '''pixel_values''': image_features.pixel_values,
                '''conditional_pixel_values''': prompt_features.pixel_values,
            }
            return encoding
         elif text is not None and images is not None:
            __snake_case							=  image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         elif visual_prompt is not None:
            __snake_case							=  {
                '''conditional_pixel_values''': prompt_features.pixel_values,
            }
            return encoding
         else:
            return BatchEncoding(data=dict(**lowercase_ )							,     tensor_type=lowercase_ )


      def   a					(self			: Optional[Any]							,     *a__			: List[Any]							,     **a__			: List[Any] ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*lowercase_							,     **lowercase_ )


      def   a					(self			: Union[str, Any]							,     *a__			: Any							,     **a__			: int ):





         """simple docstring"""



         return self.tokenizer.decode(*lowercase_							,     **lowercase_ )


      @property
      def   a					(self			: Optional[int] ):





         """simple docstring"""



         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,     lowercase_							,     )
         return self.image_processor_class


      @property
      def   a					(self			: int ):





         """simple docstring"""



         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,     lowercase_							,     )
         return self.image_processor


from math import isqrt, loga


def 				lowerCAmelCase__       (			a__ )      ->list[int]:




  '''simple docstring'''

  _UpperCamelCase           =    [True] * max_number
  for i in range(2    ,     isqrt(max_number - 1 ) + 1 ):
    if is_prime[i]:
      for j in range(i**2    ,     a__    ,     a__ ):
        _UpperCamelCase           =    False

  return [i for i in range(2    ,     a__ ) if is_prime[i]]


def 				lowerCAmelCase__       (			a__ = 800_800    ,     a__ = 800_800 )      ->int:




  '''simple docstring'''

  _UpperCamelCase           =    degree * loga(a__ )
  _UpperCamelCase           =    int(a__ )
  _UpperCamelCase           =    calculate_prime_numbers(a__ )

  _UpperCamelCase           =    0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    len(a__ ) - 1
  while left < right:
    while (
        prime_numbers[right] * loga(prime_numbers[left] )
        + prime_numbers[left] * loga(prime_numbers[right] )
        > upper_bound
    ):
      right -= 1
    hybrid_integers_count += right - left
    left += 1

  return hybrid_integers_count


if __name__ == "__main__":
   print(F"{solution() = }")




